Genome-wide association (GWA) studies have identified >100 regions of the genome that contribute to risk for schizophrenia. As observed for other complex disorders, the identified regions are overwhelmingly non- coding, strongly suggesting that genetic variation in gene regulatory elements is a major mechanistic contributor. Further investigation of those regulatory mechanisms is precluded by a fundamental gap in the ability to identify disorder-specific regulatory elements in the brain, and limited understand of how genetic variation within those elements influences their function. To address that knowledge gap, this project will comprehensively identify, characterize, and validate non-coding functional regulatory elements in brain tissues relevant to schizophrenia. The central hypothesis of the proposal is that non-coding variation contributes to schizophrenia by directly altering the function of regulatory elements in the brain. The motivation for the proposed study is that identifying regulatory mechanisms of schizophrenia has the potential to translate into improved diagnosis and treatment of this common, chronically debilitating disorder. Powered by a team with strong interdisciplinary expertise in psychiatric disorders, functional genomics, comparative primate genomics, and statistical genetics, this hypothesis will be tested by completing three specific aims: 1) Comprehensively identify active gene regulatory elements in three brain regions from 100 schizophrenia cases and 100 controls using ATAC-seq; 2) Identify chromatin QTLs (cQTLs) that impact chromatin accessibility and gene expression, and perform targeted association tests using the most up to date PGC GWA mega analysis results; 3) Prioritize and quantify regulatory variant function using high-throughput reporter-gene expression assays, and validate by genome editing. The approach is innovative because it utilizes a highly complementary and diverse set of experimental approaches to drive targeted genetic and functional investigation into the regulatory mechanisms of schizophrenia. Ultimately, the data produced and the experimental and statistical approaches developed will enable related studies of other disorders and diseases. In doing so, the proposed research provides a much-needed path forward to understand how non- coding variation contributes to complex human phenotypes.